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Iterative Min Cut Clustering Based on Graph Cuts
Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827042/ https://www.ncbi.nlm.nih.gov/pubmed/33440849 http://dx.doi.org/10.3390/s21020474 |
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author | Liu, Bowen Liu, Zhaoying Li, Yujian Zhang, Ting Zhang, Zhilin |
author_facet | Liu, Bowen Liu, Zhaoying Li, Yujian Zhang, Ting Zhang, Zhilin |
author_sort | Liu, Bowen |
collection | PubMed |
description | Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time. |
format | Online Article Text |
id | pubmed-7827042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78270422021-01-25 Iterative Min Cut Clustering Based on Graph Cuts Liu, Bowen Liu, Zhaoying Li, Yujian Zhang, Ting Zhang, Zhilin Sensors (Basel) Communication Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time. MDPI 2021-01-11 /pmc/articles/PMC7827042/ /pubmed/33440849 http://dx.doi.org/10.3390/s21020474 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Liu, Bowen Liu, Zhaoying Li, Yujian Zhang, Ting Zhang, Zhilin Iterative Min Cut Clustering Based on Graph Cuts |
title | Iterative Min Cut Clustering Based on Graph Cuts |
title_full | Iterative Min Cut Clustering Based on Graph Cuts |
title_fullStr | Iterative Min Cut Clustering Based on Graph Cuts |
title_full_unstemmed | Iterative Min Cut Clustering Based on Graph Cuts |
title_short | Iterative Min Cut Clustering Based on Graph Cuts |
title_sort | iterative min cut clustering based on graph cuts |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827042/ https://www.ncbi.nlm.nih.gov/pubmed/33440849 http://dx.doi.org/10.3390/s21020474 |
work_keys_str_mv | AT liubowen iterativemincutclusteringbasedongraphcuts AT liuzhaoying iterativemincutclusteringbasedongraphcuts AT liyujian iterativemincutclusteringbasedongraphcuts AT zhangting iterativemincutclusteringbasedongraphcuts AT zhangzhilin iterativemincutclusteringbasedongraphcuts |